COVID-19 Analysis
Project by:
PRASFUR TIWARI
DIVYANSH TIWARI
SONAM KUMAR
About COVID-19
Short for Corona Virus disease 2019, COVID-19 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first identified in December 2019 in Wuhan, China, and has since spread globally, resulting in an ongoing pandemic.
For more details about COVID-19, visit its wikipedia page from here.
About Our Project
In this project, we intent to analyse the effects of COVID-19 in India, before lockdown. We will be analyzing the total number of confirmed, cured and death cases. Our analysis will be based on 4 main factors:
- Day-wise Analysis
- State-wise Analysis
- Population-wise Analysis
- Age-wise Analysis
Also, we will be predicting the possible number of infected cases if there was no/delayed lockdown, so as to estimate the success of lockdown.
Data Analysis
Importing needed modules
In [3]:
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from datetime import datetime
import folium
from geopy.geocoders import Nominatim
import requests
import warnings
warnings.filterwarnings('ignore')
#from pylab import rcParams
Importing Necessary CSVs and Converting them to pandas dataframe
In [4]:
df_population=pd.read_csv('https://raw.githubusercontent.com/DivyT-03/Project/master/population_india_census2011.csv')
df_population.head()
Out[4]:
In [5]:
df_main=pd.read_csv('https://raw.githubusercontent.com/DivyT-03/Project/master/covid_19_india(16.4.20).csv')
df_main.head()
Out[5]:
In [6]:
df_main.dtypes
Out[6]:
Removing Unnecessary Columns
In [7]:
df=df_main[["Date","State/UnionTerritory","Cured","Deaths","Confirmed"]]
df.head()
Out[7]:
Creating List of States, Dates and Columns to make the final dataframe
In [8]:
state=df_population['State / Union Territory'].tolist()
state.sort()
print("Total states + union territories:",len(state))
In [9]:
dates=list(df.Date.unique())
print("Total days:",len(dates))
Creating Various Lists of Values to be inserted in Dataframe columns
In [10]:
col=["Day","State_ID","Cured","Deaths","Confirmed"]
Date_ID=[]
State_ID=[]
Cured=[]
Deaths=[]
Confirmed=[]
for d_id,d in enumerate(dates,1):
for s_id,s in enumerate(state,1):
df_val=df[df["Date"]==d]
df_val=df_val[df_val["State/UnionTerritory"]==s]
Date_ID.append(d_id)
State_ID.append(s_id)
try:
Cu=df_val.Cured.to_list()[0]
De=df_val.Deaths.to_list()[0]
Co=df_val.Confirmed.to_list()[0]
except:
Cu=0
De=0
Co=0
Cured.append(Cu)
Deaths.append(De)
Confirmed.append(Co)
Making the final dataframe
In [11]:
list_of_tuples = list(zip(Date_ID,State_ID,Cured,Deaths,Confirmed))
df_cv = pd.DataFrame(list_of_tuples, columns = col)
df_cv.head()
Out[11]:
In [12]:
print("Our dataset has {} rows and {} columns".format(df_cv.shape[0],df_cv.shape[1]))
Data Cleaning
In [13]:
df_cv.isnull()
Out[13]:
In [14]:
df_cv.isnull().sum()
Out[14]:
In [15]:
import seaborn as sns
plt.rcParams['figure.figsize']=7,5
sns.heatmap(df_cv.isnull(),yticklabels=False)
plt.show()
As there are no null values in our dataframe, we do not need to perform any clean-up.
Descriptive Statistics
In [16]:
df_cv.info()
In [17]:
df_cv.describe()
Out[17]:
In [18]:
df_cv.dtypes
Out[18]:
In [19]:
population=[]
for s in state:
df_val=df_population[df_population["State / Union Territory"]==s]
Po=df_val.Population.tolist()
population.append(Po)
list_of_tuples = list(zip(state,State_ID,population))
df_stateinfo = pd.DataFrame(list_of_tuples, columns = ["State","State ID", "Population"])
df_stateinfo.head()
Out[19]:
Correlation
In [20]:
df_cv.corr()
Out[20]:
In [21]:
from scipy import stats
pearson_coef, p_value = stats.pearsonr(df_cv['Day'], df_cv['Confirmed'])
print("The Pearson Correlation Coefficient is", pearson_coef, " with a P-value of P =", p_value)
Observation:
Since the p-value is < 0.001, the correlation between Day and Confirmed Cases is statistically significant, and the linear relationship isn't quite strong (~0.293).In [22]:
pearson_coef, p_value = stats.pearsonr(df_cv['Day'], df_cv['Cured'])
print("The Pearson Correlation Coefficient is", pearson_coef, " with a P-value of P =", p_value)
Observation:
Since the p-value is < 0.001, the correlation between Day and Cured Patients is statistically significant, and the linear relationship isn't quite strong (~0.333).In [23]:
pearson_coef, p_value = stats.pearsonr(df_cv['Day'], df_cv['Deaths'])
print("The Pearson Correlation Coefficient is", pearson_coef, " with a P-value of P =", p_value)
Observation
Since the p-value is < 0.001, the correlation between Day and Deaths is statistically significant, and the linear relationship isn't quite strong (~0.244).In [24]:
df_cv.head()
Out[24]:
Data Visualization
We take the data of only 56 days from the beginning, i.e. data till first lockdown, and keep the rest of the data for prediction.
In [25]:
df_cv1=df_cv[df_cv['Day']<57]
In [24]:
# Plotting the heatmap of the data set
plt.figure(figsize=(10,8))
sns.heatmap(df_cv1.corr(),linewidths=3)
plt.title('Heatmap')
plt.show()
In [27]:
fig=plt.figure()
ax=fig.add_subplot(111,projection='3d')
ax.scatter(df_cv1['Confirmed'],df_cv1['Cured'],df_cv1['Deaths'],c='b',marker='o')
ax.set_xlabel('Confirmed')
ax.set_ylabel('Cured')
ax.set_zlabel('Deaths')
plt.show()
In [25]:
# Relation between Confirmed Cases and Days
plt.figure(1,figsize=(10,5))
sns.regplot(df_cv1['Confirmed'],df_cv1['Day'],color='aqua')
plt.title('Confirmed Vs Days',size=20)
plt.show()
In [26]:
# Scatter Plot Between Confirmed Cases and Days
plt.figure(1,figsize=(20,10))
df_cv1.plot(kind='scatter',y='Confirmed',x='Day',alpha=.5)
plt.xlabel('Days',size=15)
plt.ylabel('Confirmed',size=15)
plt.title('Confirmed Vs Days Scatter Plot',size=20)
plt.show()
In [27]:
# Relation between Confirmed Cases and Cured Cases
plt.figure(1,figsize=(10,5))
sns.regplot(df_cv1['Confirmed'],df_cv1['Cured'],color='orange')
plt.title('Confirmed Vs Cured',size=20)
plt.show()
In [28]:
# Relation between Confirmed Cases and Deaths
plt.figure(1,figsize=(10,5))
sns.regplot(df_cv1['Confirmed'],df_cv1['Deaths'],color='Blue')
plt.title('Confirmed Vs Deaths',size=20)
plt.show()
In [29]:
# Relation between Cured Cases and Days
plt.figure(1,figsize=(10,5))
sns.regplot(df_cv1['Cured'],df_cv1['Day'],color='LightGreen')
plt.title('Cured Vs Days',size=20)
plt.show()
In [30]:
# Relation between Cured Cases and Deaths
plt.figure(1,figsize=(10,5))
sns.regplot(df_cv1['Cured'],df_cv1['Deaths'],color='Red')
plt.title('Cured Vs Deaths',size=20)
plt.show()
In [31]:
# Relation between Deaths and Days
plt.figure(1,figsize=(10,5))
sns.regplot(df_cv1['Deaths'],df_cv1['Day'],color='Blue')
plt.title('Days Vs Deaths',size=20)
plt.show()
Day-wise analysis
In [32]:
df2=df_cv1.copy()
df2.drop(columns=["State_ID"],axis=1,inplace=True)
df2=df2.groupby(['Day']).sum()
df2.reset_index(inplace=True)
df2.head()
Out[32]:
1. Confirmed Cases' Plots
In [33]:
plt.figure(figsize=(35,20))
plt.bar(df2['Day'],df2['Confirmed'],align='center',alpha=0.5,color='purple')
plt.title("Rate vs Date",size=40)
plt.xlabel("Day",size=35)
plt.xticks(fontsize=20,fontname='monospace')
plt.ylabel("Cases",size=35)
plt.yticks(fontsize=30,fontname='monospace')
plt.show()
In [34]:
print("Confirmed cases:",df2['Confirmed'].max())
The curve depicts the exponential increase in the infected rates.
2. Cured Rate's Plot
In [35]:
d3=df2.copy()
plt.figure(figsize=(35,20))
plt.bar(d3['Day'],d3['Cured'],align='center',alpha=0.5,color="Green")
plt.title("Rate vs Date",size=40)
plt.xlabel("Day",size=35)
plt.xticks(fontsize=20,fontname='monospace')
plt.ylabel("Cases",size=35)
plt.yticks(fontsize=30,fontname='monospace')
plt.show()
3. Death Rate's Plots
In [36]:
d4=df2.copy()
plt.figure(figsize=(35,20))
plt.bar(d4['Day'],d4['Deaths'],align='center',alpha=0.5,color="Red")
plt.title("Rate vs Date",size=40)
plt.xlabel("Day",size=35)
plt.xticks(fontsize=20,fontname='monospace')
plt.ylabel("Deaths",size=35)
plt.yticks(fontsize=30,fontname='monospace')
plt.show()
From the plots, it's utterly significant that the confirmed, cured and the death rates rose exponentially.
In [37]:
a=df2['Confirmed'].max()
b=df2['Cured'].max()
c=df2['Deaths'].max()
d=df2['Cured'].max()/df2['Confirmed'].max()*100
e=df2['Deaths'].max()/df2['Confirmed'].max()*100
A={"Feature":["Total Infected","Total Cured","Total Deaths","Cured %","Death %"],"Analysis":[a,b,c,d,e]}
Analysis=pd.DataFrame(A)
Analysis.Analysis=Analysis.Analysis.astype('int64')
Analysis.set_index(['Feature','Analysis'],inplace=True)
Analysis
Out[37]:
State-wise analysis
In [38]:
df3=df_cv[df_cv['Day']<57]
df3.drop(columns=['Day'],axis=1,inplace=True)
df3=df3.groupby(['State_ID']).max()
df3.reset_index(inplace=True)
df3['State_Name']=df_stateinfo['State']
df3.head()
Out[38]:
1. Confirmed Cases' Plots
In [39]:
d5=df3.copy()
plt.figure(figsize=(35,20))
plt.bar(df3['State_Name'],df3['Confirmed'],align='center',alpha=0.5,color="Blue")
plt.title("Case Count vs State",size=40)
plt.xlabel("State",size=35)
plt.xticks(fontsize=20,fontname='monospace',rotation=90)
plt.ylabel("Cases",size=35)
plt.yticks(fontsize=30,fontname='monospace')
plt.show()
d5.sort_values(["Confirmed"],axis=0,ascending=False,inplace=True)
print("Top 5 most infected states:\n")
d5[['State_Name','Confirmed']].head()
Out[39]:
In [40]:
import plotly.graph_objects as go
fig1=go.Figure(data=go.Scatterpolar(r=d5['Confirmed'].head(),theta=d5['State_Name'].head(),fill='toself'))
fig1.update_layout(polar=dict(radialaxis=dict(visible=True),),showlegend=False)
fig1.show()
In [41]:
# Boxplot for confirmed cases per state
plt.figure(figsize=(7,5))
df3.boxplot(['Confirmed'],grid=True,fontsize=15,vert=False,showfliers=False)
plt.show()
2. Cured Rate's Plot
In [42]:
d6=df3.copy()
plt.figure(figsize=(35,20))
plt.bar(df3['State_Name'],df3['Cured'],align='center',alpha=0.7,color="Orange")
plt.title("Cured vs State",size=40)
plt.xlabel("State",size=35)
plt.xticks(fontsize=20,fontname='monospace',rotation=90)
plt.ylabel("Cured",size=35)
plt.yticks(fontsize=30,fontname='monospace')
plt.show()
d6.sort_values(["Cured"],axis=0,ascending=False,inplace=True)
print("Top 5 states with most cured cases:\n")
d6[['State_Name','Cured']].head()
Out[42]:
3. Death Rate's Plot
In [43]:
d7=df3.copy()
plt.figure(figsize=(35,20))
plt.bar(df3['State_Name'],df3['Deaths'],align='center',alpha=0.9,color="Red")
plt.title("Deaths vs State",size=40)
plt.xlabel("State",size=35)
plt.xticks(rotation=90,fontsize=20,fontname='monospace')
plt.ylabel("Deaths",size=35)
plt.yticks(fontsize=30,fontname='monospace')
plt.show()
d7.sort_values(["Deaths"],axis=0,ascending=False,inplace=True)
print("Top 5 states with most deaths:\n")
d7[['State_Name','Deaths']].head()
Out[43]:
In [44]:
# Pie chart depiction of deaths in top 5 states
plt.figure()
fig,ax=plt.subplots(figsize=(7,7))
plt.subplots_adjust(left=0.5,wspace=0.2)
ax.pie(d7['Deaths'].head(),explode=(0.1,0,0,0,0),labels=d7['State_Name'].head(),autopct='%1.1f%%',shadow=True,startangle=90)
ax.axis('equal')
plt.legend(d7['State_Name'].head(),loc="best")
plt.tight_layout()
plt.show()
Overall Scenario using Map
In [45]:
only_state=df3.copy()
only_state.drop([0,5,7,17,18,26,31],inplace=True)
In [46]:
lat=[]
lng=[]
geolocator=Nominatim(user_agent='foursquare_api')
for nm in only_state['State_Name']:
location=geolocator.geocode(str(nm),timeout=10)
lat.append(location.latitude)
lng.append(location.longitude)
In [47]:
only_state['Latitude']=lat
only_state['Longitude']=lng
In [48]:
wm=folium.Map(zoom_start=5,location=[only_state['Latitude'].mean(),only_state['Longitude'].mean()])
mp=folium.map.FeatureGroup()
for i,j,k,l,m in zip(only_state['Latitude'],only_state['Longitude'],only_state['Confirmed'],only_state['Cured'],only_state['Deaths']):
mp.add_child(folium.CircleMarker(location=[i,j],radius=5,color='red',fill_color='Yellow'))
folium.Marker([i,j],popup='Confirmed:'+str(k)+'\nCured:'+str(l)+"\nDeaths:"+str(m)).add_to(mp)
wm.add_child(mp)
wm
Out[48]:
Population-wise Analysis
In [49]:
pop=[]
for p in range(0,len(df_stateinfo)):
pop.append(df_stateinfo['Population'][p][0])
df_pop=df3.copy()
df_pop['Population']=pop
df_pop.head()
Out[49]:
Most populated states in India
In [50]:
most=df_pop.copy()
most.sort_values(["Population"],axis=0,ascending=False,inplace=True)
plt.figure(figsize=(35,20))
plt.bar(most['State_Name'].head(10),most['Population'].head(10),align='center',alpha=0.9,color="purple")
plt.title("Population vs State",size=40)
plt.xlabel("State",size=35)
plt.xticks(fontsize=20,fontname='monospace')
plt.ylabel("Population",size=35)
plt.yticks(fontsize=30,fontname='monospace')
plt.show()
print("Top 5 most populated states:")
most[['State_Name','Population']].head()
Out[50]:
In [51]:
most['Confirmed %']=round((most['Confirmed']/most['Population'])*100,9)
most['Cured %']=round(most['Cured']/most['Population']*100,9)
most['Death %']=round(most['Deaths']/most['Population']*100,9)
most.head()
Out[51]:
1. Population Vs Confirmed
In [52]:
# Relation between Population and Confirmed
most1=most.copy()
plt.figure(1,figsize=(10,5))
sns.regplot(most1['Population'],most1['Confirmed'],color='brown')
plt.title('Population Vs Confirmed',size=20)
plt.show()
most1.sort_values(["Confirmed %"],axis=0,ascending=False,inplace=True)
print("States with most affected population: ")
most1[['State_Name','Confirmed','Confirmed %']].head()
Out[52]:
2. Population Vs Cured
In [53]:
# Relation between Population and Cured
most2=most.copy()
plt.figure(1,figsize=(10,5))
sns.regplot(most2['Population'],most2['Cured'],color='magenta')
plt.title('Population Vs Confirmed',size=20)
plt.show()
most2.sort_values(["Cured %"],axis=0,ascending=False,inplace=True)
print("States with most cured population: ")
most2[['State_Name','Cured','Cured %']].head()
Out[53]:
3. Population Vs Deaths
In [54]:
# Relation between Population and Deaths
most3=most.copy()
plt.figure(1,figsize=(10,5))
sns.regplot(most3['Population'],most3['Deaths'],color='black')
plt.title('Population Vs Deaths',size=20)
plt.show()
most3.sort_values(["Death %"],axis=0,ascending=False,inplace=True)
print("States with most deaths: ")
most3[['State_Name','Deaths','Death %']].head()
Out[54]:
Age-Wise Analysis
This data is of starting 60 days, i.e., till 28-03-2020.
In [55]:
age=pd.read_csv('https://raw.githubusercontent.com/DivyT-03/Project/master/AgeGroupDetails.csv')
age
Out[55]:
Cleaning Data
In [56]:
age.drop([9],inplace=True)
Visualization
In [57]:
# Boxplot for cases per age-group
plt.figure(figsize=(7,5))
age.boxplot(['TotalCases'],grid=False,fontsize=15)
plt.show()
In [58]:
plt.figure(figsize=(35,20))
plt.bar(age['AgeGroup'],age['TotalCases'],align='center',alpha=0.9)
plt.title("Age-Group Vs Cases",size=40)
plt.xlabel("Age-Group",size=35)
plt.xticks(fontsize=20,fontname='monospace',rotation=90)
plt.ylabel("Cases",size=35)
plt.yticks(fontsize=30,fontname='monospace')
plt.show()
age1=age.copy()
age1.sort_values(["TotalCases"],axis=0,ascending=False,inplace=True)
age1.drop(columns={'Sno'},inplace=True)
age1.set_index(['AgeGroup'],inplace=True)
print("Mostly affected age-groups are:")
age1.head(2)
Out[58]:
Data Modelling
As per the plots we obtained above, we intent to use the exponential regression model to predict the infected, cured and death rates.
Regression
Day-Wise Prediction
In [59]:
df2.hist(['Confirmed','Cured','Deaths'])
plt.show()
Creating Training/Testing Datasets
In [137]:
import numpy as np
msk=np.random.rand(len(df2))<.75
train=df2[msk]
test=df2[~msk]
Transforming Data
In [138]:
train_x=train['Day'].values
test_x=test['Day'].values
train_y1=train['Confirmed'].values
test_y1=test['Confirmed'].values
train_y2=train['Cured'].values
test_y2=test['Cured'].values
train_y3=train['Deaths'].values
test_y3=test['Deaths'].values
Defining the exponential function
In [62]:
def sigmoid(x,a,b):
y=a*np.exp(b*x)
return y
Finding optimized parameters
In [139]:
'''This function has a particular limit, hence we find the best parameters only limited number of times. Hence this code is a comment'''
from scipy.optimize import curve_fit
#popt1,pcov1=curve_fit(sigmoid,train_x,train_y1)
#popt2,pcov2=curve_fit(sigmoid,train_x,train_y2)
popt3,pcov3=curve_fit(sigmoid,train_x,train_y3)
In [140]:
a1=0.024636474551803556
b1=0.1806252301712788
a2=0.006491019205331347
b2=0.15694857422845598
a3=-8.509657270186663e-16
b3=0.9999999967317126
print("Optimized parameters are:")
print("\nFor Confirmed:")
print("a:",a1)
print("b:",b1)
print("\nFor Cured:")
print("a:",a2)
print("b:",b2)
print("\nFor Deaths:")
print("a:",popt3[0])
print("b:",popt3[1])
Prediction
In [141]:
pred1=sigmoid(test_x,a1,b1)
pred2=sigmoid(test_x,a2,b2)
pred3=sigmoid(test_x,popt3[0],popt3[1])
Comparing Predicted and Actual Values
1. Confirmed Cases
In [111]:
plt.figure(figsize=(7,7))
plt.plot(test_x,pred1)
plt.plot(test_x,test_y1)
plt.show()
Predicted values
In [112]:
pred1
Out[112]:
Evaluation
In [113]:
from sklearn.metrics import r2_score
r1=r2_score(pred1,test_y1)
rss1=round(np.mean((pred1-test['Confirmed'])**2),3)
mae1=round(np.mean(np.absolute(pred1-test['Confirmed'])),3)
print("For Confirmed:\n")
print("R-2 Score:",round(r1*100,2),'%')
print("Residual Sum of Squares:",rss1)
print("Mean Absolute Error:",mae1)
2. For Cured Cases
In [114]:
plt.figure(figsize=(7,7))
plt.plot(test_x,pred2)
plt.plot(test_x,test_y2)
plt.show()
Predicted values
In [115]:
pred2
Out[115]:
Evaluation
In [116]:
r2=r2_score(pred2,test_y2)
rss2=round(np.mean((pred2-test['Cured'])**2),3)
mae2=round(np.mean(np.absolute(pred2-test['Cured'])),3)
print("For Cured:\n")
print("R-2 Score:",round(r2*100,2),'%')
print("Residual Sum of Squares:",rss2)
print("Mean Absolute Error:",mae2)
3. For Deaths
In [163]:
plt.figure(figsize=(7,7))
plt.plot(test_x,pred3*(-1))
plt.plot(test_x,test_y3*(2900000))
plt.show()
Predicted Values
In [160]:
pred3*(-2900000)
Out[160]:
Evaluation
In [159]:
r3=r2_score(pred3,test_y3*(-2900000))
rss3=round(np.mean((pred3-test['Deaths'])**2),3)
mae3=round(np.mean(np.absolute(pred3-test['Deaths'])),3)
print("For Deaths:\n")
print("R-2 Score:",round(r3*100,2),'%')
print("Residual Sum of Squares:",rss3)
print("Mean Absolute Error:",mae3)
Summary
In [164]:
summ={'Factor':['Confirmed','Cured','Deaths'],'R2 %':[round(r1*100,2),round(r2*100,2),round(r3*100,2)]}
rep=pd.DataFrame(summ)
rep.set_index(['Factor','R2 %'],inplace=True)
rep
Out[164]:
We will be continuing to predict the confirmed cases post lock-down and see the results.
Predicting cases after lock-down (till 108 days)
Since we got a good percentage of confirmed and cured rates, we will be predicting cases only for these two factors.
1. Confirmed Cases
In [76]:
post_ld=[]
for x in range(57,109):
post_ld.append(int(sigmoid(x,a1,b1)))
In [77]:
df_cv2=df_cv[df_cv['Day']>=57]
df_cv2.drop(columns={'State_ID'},inplace=True)
df_cv2=df_cv2.groupby(['Day']).sum()
df_cv2.reset_index(inplace=True)
df_preds=df_cv2[['Day','Confirmed']]
df_preds['Predicted']=post_ld
df_preds.head()
Out[77]:
Visual Comparison
In [78]:
plt.figure(figsize=(35,20))
plt.bar(df_preds['Day'],df_preds['Confirmed'],align='center',alpha=0.9)
plt.title("Confirmed Cases Vs Days",size=40)
plt.xlabel("Days (From 57th Day)",size=35)
plt.xticks(fontsize=20,fontname='monospace',rotation=90)
plt.ylabel("Cases",size=35)
plt.yticks(fontsize=30,fontname='monospace')
plt.show()
In [79]:
plt.figure(figsize=(35,20))
plt.bar(df_preds['Day'],df_preds['Predicted'],align='center',alpha=0.9,color='red')
plt.title("Predicted Confirmed Cases Vs Days",size=40)
plt.xlabel("Days (From 57th Day)",size=35)
plt.xticks(fontsize=20,fontname='monospace',rotation=90)
plt.ylabel("Cases",size=35)
plt.yticks(fontsize=30,fontname='monospace')
plt.show()
In [165]:
plt.plot(df_preds['Day'],df_preds['Confirmed'])
plt.plot(df_preds['Day'],df_preds['Predicted'])
plt.show()
This signifies predicted values are very large as compared to the actual ones.
Observation
In [166]:
df_preds.hist(['Confirmed','Predicted'])
plt.show()
print("Maximum cases during lockdown:",df_preds['Confirmed'].max())
print("Maximum cases if there was no lockdown:",df_preds['Predicted'].max())
print("Percentage Population Saved:",round((df_preds['Predicted'].max()-df_preds['Confirmed'].max())/df_preds['Predicted'].max()*100,2),"%")
Result
Our estimations show that due to lockdown, 98.83% of India's population was saved from being infected by COVID-19. This clearly signifies that lockdown was successful in its implementation, and proves out to be a correct decision, obviously!